Using data to build machine learning (ML) models

In this section, you will read about techniques for efficient (re)training, inferencing, deployment, and customizing ML models. We will also discuss what has prevented high levels of demand from being met, and what is being done to resolve that.

Before we dive into the topic, it’s appropriate to briefly review Artificial Intelligence (AI) and what distinguishes it from ML and Deep Learning (DL). IBM describes AI as “leverage[ing] computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” See “What is Artificial Intelligence (AI)?” in the Suggested pre-reading material section at the beginning of the chapter for a deeper explanation and some background history. ML is a branch of AI and a component of the field of data science that uses data and algorithms to imitate the way we believe human brains acquire knowledge. ML typically uses structured or labeled...